A Low Cost Implementation of Multi-label Classification Algorithm Using Mathematica on Raspberry Pi

John, Neethu; Surya, R.; Ashwini, R.; Kumar, Sachin; Soman, K P

doi:10.1016/j.procs.2015.02.025

Cited by 10 publications

(5 citation statements)

References 3 publications

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“…The C++ language's powerful libraries and packages [24]. Raspberry Pi is a portable Linux-based embedded system board that has been used in many computer vision, image classification, and machine learning applications [23]. Some versions of Raspberry Pi have a Quad-core CPU, support the parallel processing implementations using OpenMP [25] and MPI [26] as discussed in [27] also in [28] to solve edge detection problems and utilize the cluster to accelerate the wavelet transform in 3D.…”

Section: Preliminaries a Internet Of Medical Things (Iomt)mentioning

confidence: 99%

Multiple Zero-Watermarking of Medical Images for Internet of Medical Things

et al. 2022

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The Internet of Medical Things (IoMT) plays a vital role in healthcare systems to increase electronic devices' accuracy, reliability, and productivity. This paper presents fast multiple zero-watermarking methods based on Multi-channel Fractional Legendre Fourier moments (MFrLFMs) for medical image security and copyright protection in IoMT applications without deforming the original medical images. The MFrLFMs are utilized due to their high accuracy, numerical stability, geometric invariances, and high resistance to various attacks. Based on the most significant features generated from MFrLFMs, after scrambling using a two-dimensional Discrete Henon Map, then XORed with binary scrambled watermark to construct owner share. The proposed watermarking method is implemented using a low-cost Raspberry Pi Linux microprocessor, which ensures the suitability of medical devices in the IoMT environment. We evaluated the robustness of the proposed algorithm against different geometric and common signal processing attacks using various medical images. The proposed method gives better BER, NC, and SSIM values than existing methods. INDEX TERMSColor medical images, zero-Watermarking, IoMT, fractional-order moments, Raspberry-Pi.

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Section: Preliminaries a Internet Of Medical Things (Iomt)mentioning

confidence: 99%

Multiple Zero-Watermarking of Medical Images for Internet of Medical Things

et al. 2022

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“…It is a cost-effective, lightweight, and portable computer. Raspberry Pi has been utilized in several machine learning applications such as computer vision and image classification [ 21 ].…”

Section: Preliminariesmentioning

confidence: 99%

COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi

Hosny

Darwish

et al. 2021

PLoS ONE

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The diagnosis of COVID-19 is of vital demand. Several studies have been conducted to decide whether the chest X-ray and computed tomography (CT) scans of patients indicate COVID-19. While these efforts resulted in successful classification systems, the design of a portable and cost-effective COVID-19 diagnosis system has not been addressed yet. The memory requirements of the current state-of-the-art COVID-19 diagnosis systems are not suitable for embedded systems due to the required large memory size of these systems (e.g., hundreds of megabytes). Thus, the current work is motivated to design a similar system with minimal memory requirements. In this paper, we propose a diagnosis system using a Raspberry Pi Linux embedded system. First, local features are extracted using local binary pattern (LBP) algorithm. Second, the global features are extracted from the chest X-ray or CT scans using multi-channel fractional-order Legendre-Fourier moments (MFrLFMs). Finally, the most significant features (local and global) are selected. The proposed system steps are integrated to fit the low computational and memory capacities of the embedded system. The proposed method has the smallest computational and memory resources,less than the state-of-the-art methods by two to three orders of magnitude, among existing state-of-the-art deep learning (DL)-based methods.

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“…For instance, machine learning applications within a vehicle [8], at face detection and tracking [9], and in botany [10]. Other similar works taking advantage of Raspberry Pis have also been explored [11][12][13][14][15][16][17].…”

Section: Related Workmentioning

confidence: 99%

Deploying a Task-based Runtime System on Raspberry Pi Clusters

Gupta,

Brandt,

Wagle

et al. 2020

Preprint

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Arm ® technology is becoming increasingly important in HPC. Recently, Fugaku, an Arm ® -based system, was awarded the number one place in the Top500 list. Raspberry Pis provide an inexpensive platform to become familiar with this architecture. However, Pis can also be useful on their own. Here we describe our efforts to configure and benchmark the use of a Raspberry Pi cluster with the HPX/Phylanx platform (normally intended for use with HPC applications) and document the lessons we learned. First, we highlight the required changes in the configuration of the Pi to gain performance. Second, we explore how limited memory bandwidth limits the use of all cores in our shared memory benchmarks. Third, we evaluate whether low network bandwidth affects distributed performance. Fourth, we discuss the power consumption and the resulting trade-off in cost of operation and performance.

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A Low Cost Implementation of Multi-label Classification Algorithm Using Mathematica on Raspberry Pi

Cited by 10 publications

References 3 publications

Multiple Zero-Watermarking of Medical Images for Internet of Medical Things

Multiple Zero-Watermarking of Medical Images for Internet of Medical Things

COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi

Deploying a Task-based Runtime System on Raspberry Pi Clusters

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